3D Printed Tooling for Injection Molded Microfluidics

Microfluidics have been used for several decades to conduct a wide range of research in chemistry and the life sciences. The reduced dimensions of these devices give them advantages over classical analysis techniques such as increased sensitivity, shorter analysis times, and lower reagent consumption. However, current manufacturing processes for microfluidic chips either limit them to materials with unwanted properties, or are not cost-effective for rapid-prototyping approaches. Here the authors show that inlays for injection moulding can be 3D printed, thus reducing the skills, cost, and time required for tool fabrication. They demonstrate the importance of orientation of the part during 3D printing so that features as small as 100 × 200 µm can be printed. They also demonstrate that the 3D printed inlay is durable enough to fabricate at least 500 parts. Furthermore, devices can be designed, manufactured, and tested within one working day. Finally, as demonstrators they design and mould a microfluidic chip to house a plasmonic biosensor as well as a device to house liver organoids showing how such chips can be used in organ-on-a-chip applications. This new fabrication technique bridges the gap between small production and industrial scale manufacturing, while making microfluidics cheaper, and more widely accessible.     

Influence of model melanoidins on calcium-dependent transport mechanisms in smooth muscle tissue

Melanoidins obtained from L-arginine and D-glucose (MW > 3500 Da) were tested for their ability to influence the contractility of gastric smooth muscles. A study within the range 0.1-10 mg/mL revealed that at low concentrations, the melanoidins provoked concentration-dependent contraction, whereas a muscle relaxation was registered at high concentrations. The contraction was preceded by changes in the calcium membrane current as measured by single sucrose-gap method and significantly attenuated by the calcium channel blockers D-600 and nifedipine. Measurements with Ca(2+)-selective electrode showed that the melanoidins decreased the concentration of ionized Ca(2+ )in tissue bath in concentration-dependent manner. Experiments carried out in solutions with lower than normal Ca(2+) concentration and using melanoidins preliminary saturated with Ca(2+ )confirmed that the calcium chelation by melanoidins was a key contributing cause for the development of relaxant response. The results obtained showed that the melanoidins could influence the contractility of smooth muscles through at least two pathways: at low concentrations they caused depolarization and activation of L-type calcium channels, stimulated the Ca(2+ )influx, and provoked contraction, whereas at high concentrations calcium binding by melanoidins led to significant depletion of extracellular calcium ions and contributed to the relaxation process observed.     

Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance

Accurate areal measurements of snow cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is considered either snow-covered or snow-free. Fractional snow cover (FSC) mapping can achieve a more precise estimate of areal snow cover extent by estimating the fraction of a pixel that is snow-covered. The most common snow fraction methods applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images have been spectral unmixing and an empirical Normalized Difference Snow Index (NDSI). Machine learning is an alternative for estimating FSC as artificial neural networks (ANNs) have been successfully used for estimating the subpixel abundances of other surfaces. The advantages of ANNs are that they can easily incorporate auxiliary information such as land cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed a multilayer feed-forward ANN trained through backpropagation to estimate FSC using MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with higher spatial-resolution FSC maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow cover maps. Testing of the network was accomplished over training and independent test areas. The developed network performed adequately with RMSE of 12% over training areas and slightly less accurately over the independent test scenes with RMSE of 14%. The developed ANN also compared favorably to the standard MODIS FSC product. The study also presents a comprehensive validation of the standard MODIS snow fraction product whose performance was found to be similar to that of the ANN.     

High‐Molecular‐Weight Polyoxirane Copolymers and their Use in High‐Performance Dye‐Sensitized Solar Cells

Low‐crystalline random and gradient P(EO‐co‐PO) copolymers and amorphous PPO and PBO of high molecular weight were synthesized by anionic coordination polymerization. Polymer gel electrolytes based on these (co)polymers were prepared and tested for long‐term performance of DSSC. The DSSC based on P(EO‐co‐PO) copolymers have longer life time compared to the homo‐PEO‐ and homo‐PPO‐based DSSC, respectively. The cells containing the chemically crosslinked copolymer gel exhibited a high efficiency of 6% after 25 d performance, whereas the solar cells based on physically crosslinked copolymer gel showed fast degradation.